Lead Consultant - Security Amazon Web Services, India
Generative AI is revolutionizing industries worldwide, offering unprecedented productivity and creative capabilities through advanced large language models. As organizations rapidly adopt this transformative technology, cybersecurity practitioners face considerable challenges in assessing risks, governance, and controls.This presentation introduces a Security Scoping Matrix for understanding generative AI security by exploring different deployment scenarios. The responsibility model for securing generative AI applications will differ according to deployment types between the LLM provider and application consumer.We will explore key considerations for security leaders and practitioners to prioritize when securing generative AI workloads. The session aims to equip cybersecurity professionals with practical approaches to navigating the complex landscape of generative AI security.
Learning Objectives:
Define precise security responsibilities between AI model providers and consumers, ensuring a holistic approach to protecting generative AI systems through well-defined accountability, compliance guidelines, and collaborative security practices.
Design flexible security approaches that can dynamically address the unique challenges of generative AI technologies, focusing on continuous monitoring, threat detection, and proactive risk mitigation across various deployment scenarios.
Create a structured framework for assessing and managing security risks across different generative AI deployments, enabling organizations to systematically evaluate potential vulnerabilities, compliance requirements, and shared responsibility models.